Architectural tactics to achieve quality attributes of machine-learning-enabled systems: a systematic literature review

Machine-learning-enabled systems are becoming increasingly common in different industries. Due to the impact of uncertainty and the pronounced role of data, ensuring the quality of such systems requires consideration of several unique characteristics in addition to traditional ones. This range of quality attributes can be achieved by the implementation of specific architectural tactics. Such architectural decisions affect the further functioning of the system and its compliance with business goals. Architectural decisions have to be made with attention to possible quality trade-offs to prevent the cost of mitigating unintended side effects. A related work analysis revealed the need for a thorough study of existing architectural decisions and their impact on various quality attributes in the context of machine-learning-enabled systems. In this paper, to address this goal, we present comprehensive research on the quality of such systems, architectural tactics, and their possible quality consequences. Based on a systematic literature review of 206 primary sources, we identified 11 common quality attributes, and 16 relevant architectural tactics together along with 85 potential quality trade-offs. Our results systematize existing research in building architectures of ML-enabled systems. They can be used by software architects and researchers at the system design stage to estimate the possible consequences of decisions made.